Method and device for identifying face, and computer-readable storage medium

ABSTRACT

Aspects of the disclosure can provide method for identifying a face where multiple images to be identified are received. Each of the multiple images includes a face image part. Each face image of face images in the multiple images to be identified is extracted. An initial figure identification result of identifying a figure in the each face image is determined by matching a face in the each face image respectively to a face in a target image in an image identification library. The face images are grouped. A target figure identification result for each face image in each group is determined according to the initial figure identification result for the each face image in the each group.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims benefit of priority to ChineseApplication No. 202010600155.4 filed on Jun. 28, 2020, the disclosure ofwhich is hereby incorporated by reference in its entirety.

BACKGROUND

Algorithms for identifying a face have found an increasingly wide rangeof applications, and have a major value in industrial applications. In ascene of application, there often may be only a single picture or veryfew pictures in each user category of a database of images forretrieval, leading to low diversity of retrievals from the database ofimages for retrieval. This can greatly impact accuracy in faceidentification.

SUMMARY

The subject disclosure relates to the field of image processing.Embodiments herein provide a method and device for identifying a face,and a computer-readable storage medium.

According to an aspect herein, a method for identifying a face caninclude receiving multiple images to be identified, each of the multipleimages including a face image part, and extracting each face image offace images in the multiple images to be identified. The method canfurther include determining an initial figure identification result ofidentifying a figure in the each face image by matching a face in theeach face image respectively to a face in a target image in an imageidentification library, grouping the face images, and determining atarget figure identification result for each face image in each groupaccording to the initial figure identification result for the each faceimage in the each group.

According to an aspect herein, a device for identifying a face includesa processor and memory. The memory is configured for storinginstructions executable by the processor. The processor is can beconfigured for receiving multiple images to be identified, each of themultiple images including a face image part, and extracting each faceimage of face images in the multiple images to be identified. Theprocessor can be further configured for determining an initial figureidentification result of identifying a figure in the each face image bymatching a face in the each face image respectively to a face in atarget image in an image identification library, grouping the faceimages, and determining a target figure identification result for eachface image in each group according to the initial figure identificationresult for the each face image in the each group.

According to an aspect herein, a non-transitory computer-readablestorage medium has stored therein computer program instructions which,when executed by a processor, implement a method herein.

The above general description and elaboration below are exemplary andexplanatory, and should not be construed to limit the subjectdisclosure.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

Drawings here are incorporated in and constitute part of the subjectdisclosure, illustrate exemplary embodiments according to the subjectdisclosure, and together with the subject disclosure, serve to explainthe principle of the subject disclosure.

FIG. 1 is a flowchart of a method for identifying a face according to anexemplary embodiment.

FIG. 2 is a flowchart of determining an initial figure identificationresult of identifying a figure in the each face image by matching a facein the each face image respectively to a face in a target image in animage identification library according to an exemplary embodiment.

FIG. 3 is a diagram of distribution of face images in multiple images tobe identified.

FIG. 4 is a diagram of distribution of face images in multiple images tobe identified.

FIG. 5 is a diagram of distribution of face images in multiple images tobe identified.

FIG. 6 is a block diagram of a device for identifying a face accordingto an exemplary embodiment.

FIG. 7 is a block diagram of a device for identifying a face accordingto an exemplary embodiment.

FIG. 8 is a block diagram of a device for identifying a face accordingto an exemplary embodiment.

DETAILED DESCRIPTION

Exemplary embodiments, examples of which are illustrated in theaccompanying drawings, are elaborated below. The following descriptionrefers to the accompanying drawings, in which identical or similarelements in two drawings are denoted by identical reference numeralsunless indicated otherwise. Implementations set forth in the followingexemplary embodiments do not represent all implementations in accordancewith the subject disclosure. Rather, they are mere examples of theapparatus (i.e., device/equipment/terminal) and method in accordancewith certain aspects of the subject disclosure as recited in theaccompanying claims. The exemplary implementation modes may take onmultiple forms, and should not be taken as being limited to examplesillustrated herein. Instead, by providing such implementation modes,embodiments herein may become more comprehensive and complete, andcomprehensive concept of the exemplary implementation modes may bedelivered to those skilled in the art. Implementations set forth in thefollowing exemplary embodiments do not represent all implementations inaccordance with the subject disclosure. Rather, they are merely examplesof the apparatus and method in accordance with certain aspects herein asrecited in the accompanying claims.

It should be noted that although a term such as first, second, third maybe adopted in an embodiment herein to describe various kinds ofinformation, such information should not be limited to such a term. Sucha term is merely for distinguishing information of the same type. Forexample, without departing from the scope of the embodiments herein, thefirst information may also be referred to as the second information.Similarly, the second information may also be referred to as the firstinformation. Depending on the context, a “if” as used herein may beinterpreted as “when” or “while” or “in response to determining that”.

In addition, described characteristics, structures or features may becombined in one or more implementation modes in any proper manner. Inthe following descriptions, many details are provided to allow a fullunderstanding of embodiments herein. However, those skilled in the artwill know that the technical solutions of embodiments herein may becarried out without one or more of the details. Alternatively, anothermethod, component, device, option, etc., may be adopted. Under otherconditions, no detail of a known structure, method, device,implementation, material or operation may be shown or described to avoidobscuring aspects of embodiments herein.

A block diagram shown in the accompanying drawings may be a functionalentity which may not necessarily correspond to a physically or logicallyindependent entity. Such a functional entity may be implemented in formof software, in one or more hardware modules or integrated circuits, orin different networks and/or processor devices and/or microcontrollerdevices.

A terminal may sometimes be referred to as a smart terminal. Theterminal may be a mobile terminal. The terminal may also be referred toas User Equipment (UE), a Mobile Station (MS), etc. A terminal may beequipment or a chip provided therein that provides a user with a voiceand/or data connection, such as handheld equipment, onboard equipment,etc., with a wireless connection function. Examples of a terminal mayinclude a mobile phone, a tablet computer, a notebook computer, a palmcomputer, a Mobile Internet Device (MID), wearable equipment, VirtualReality (VR) equipment, Augmented Reality (AR) equipment, a wirelessterminal in industrial control, a wireless terminal in unmanned drive, awireless terminal in remote surgery, a wireless terminal in a smartgrid, a wireless terminal in transportation safety, a wireless terminalin smart city, a wireless terminal in smart home, etc.

FIG. 1 is a flowchart of a method for identifying a face according to anexemplary embodiment. As shown in FIG. 1, the method includes thefollowing actions.

In S11, multiple images to be identified are received. Each of themultiple images includes a face image part. The multiple images to beidentified may be multiple images to be identified when a user organizesa photo album in a mobile phone. Such images to be identified mayinclude very few figures (people). A time span corresponding to thepeople may be long.

The multiple images to be identified may be images generated in alarge-scale event such as corporate team building. In this scene, theremay be many people in such images to be identified. Furthermore, when animage to be identified is a group photo including too many people, afactor such as image capture, lighting, an angle, etc., may have a majorimpact on identification of a face in the image to be identified.

In S12, each face image of face images in the multiple images to beidentified is extracted. Each face image in an image to be identifiedmay be detected and extracted based on an existing face detectionalgorithm, such as seetaface, mtcnn, and the like.

In S13, an initial figure identification result of identifying a figurein the each face image is determined by matching a face in the each faceimage respectively to a face in a target image in an imageidentification library. A target image in an image identificationlibrary may be uploaded by a user of a number of users and correspond tothe user. Therefore, it may be determined whether a face image in animage to be identified matches a target image by comparing the faceimage to the target image, thereby determining an initial figureidentification result for the face image.

In S14, the face images are grouped. Exemplarily, face images in onegroup may correspond to one figure.

In S15, a target figure identification result for each face image ineach group is determined according to the initial figure identificationresult for the each face image in the each group. Feature images may begrouped based on a feature of each face image in images to beidentified. Accordingly, an identification result for a face image in agroup may be corrected based on both a feature of a target image in animage identification library and features of images to be identified.

With embodiments herein, multiple images to be identified are received.Each of the multiple images includes a face image part. Each face imageof face images in the multiple images to be identified is extracted. Aninitial figure identification result of identifying a figure in the eachface image is determined by matching a face in the each face imagerespectively to a face in a target image in an image identificationlibrary. The face images are grouped. A target figure identificationresult for each face image in each group is determined according to theinitial figure identification result for the each face image in the eachgroup. With a solution herein, an initial figure identification resultfor a face image in an image to be identified may be determined througha target image in an image identification library, thereby ensuring thatthe image to be identified matches the target image in the imageidentification library. Furthermore, by grouping face images in imagesto be identified, the initial figure identification result may becorrected combining features of images to be identified. Accordingly,when there are very few target images in the image identificationlibrary or target images in the image identification library are simple,an identification result for a face image is corrected based on relationamong face images in images to be identified, thereby widening a scopeto which the method for identifying a face applies, improving accuracyin face identification.

To allow a person having ordinary skill in the art to better understanda solution herein, an aforementioned option is elaborated below.

In S13, the initial figure identification result for the each face imagemay be determined by matching the face in the each face imagerespectively to the face in the target image in the image identificationlibrary as follows. As shown in FIG. 2, the option may include an optionas follows.

In S21, a facial feature of the each face image may be extracted. Afeature extraction model may be trained in advance based on a neuralnetwork. Accordingly, a facial feature of an extracted face image may beextracted based on the feature extraction model. Exemplarily, a featureof a fully connected layer of the feature extraction model may be usedas the facial feature.

In a scene of application, face image parts in different images to beidentified may differ in size. Accordingly, when a facial feature of anextracted face image is extracted directly based on a feature extractionmodel, a feature may be fuzzy or missing. Therefore, embodiments asfollows are further provided herein.

The facial feature of the each face image may be extracted as follows.The option may include an option as follows. A key point correspondingto the each face image may be acquired by performing key pointextraction on the each face image. A key point corresponding to a faceimage may be extracted through an existing key point extractionalgorithm such as Ensemble of Regression Tress (ERT), Mnemonic DescentMethod (MDM), etc.

A target face image may be acquired by correcting the each face imageaccording to the key point. The facial feature corresponding to the eachface image may be acquired by performing feature extraction according tothe target face image. The face image may be a profile image taken fromthe side of a human face. Accordingly, a face image may be correctedaccording to a key point thereof by adjusting the key point of the faceimage according to a relation among locations of key points in astandard image. Exemplarily, the standard image may be a face imagetaken from the front, as preset by a user. Accordingly, the profileimage may be converted into the face image taken from the front,acquiring the target face image, facilitating subsequent accurateextraction of a facial feature.

If a face image is large, the face image may be corrected according to akey point thereof by reducing the size of the face image according tokey points in a standard image and the key point of the face image,thereby acquiring a target face image of a size same as the standardimage. Similarly, if a face image is small, the face image may becorrected according to a key point thereof by enlarging the face imageaccording to key points in a standard image and the key point of theface image, thereby acquiring a target face image of a size same as thestandard image. In this way, it is ensured that a standard face image isinput in subsequent facial feature extraction, thereby ensuringapplicability of a feature extraction model, improving accuracy of anextracted facial feature.

A location, a size, and the like, of a face image may also be correctedthrough key points in a standard image, specifically in a mode aselaborated, which is not repeated here. Then, a facial feature may beextracted through a feature extraction model based on such astandardized target face image. For example, a feature of a fullyconnected layer of the feature extraction model may be used as thefacial feature. In this way, with a solution herein, standardizationcorrection is performed on a face image extracted from an image to beidentified, acquiring a standardized target face image. Then, a facialfeature is extracted based on the target face image, ensuringadaptability of the target face image and the feature extraction modeleffectively, thereby effectively improving accuracy of an extractedfacial feature. Furthermore, the method for identifying a face may beused to identify a face in images to be identified taken from variousangles or an image to be identified including many people, therebywidening a scope to which the method for identifying a face applies.

Then, returning to FIG. 2, in S22, a similarity between the each faceimage and the target image may be determined according to the facialfeature of the each face image and a facial feature of the target image.

Exemplarily, after a facial feature of a face image has been determined,a Euclidean distance D between the facial feature and a facial featureof a target image may be computed. Then, the similarity may be 1-D. AEuclidean distance between features may be computed according to priorart, which is not repeated here.

A facial feature of a target image may be extracted in advance in anaforementioned facial feature extraction mode, acquiring a standardizedfacial feature corresponding to the target image, so as to match andidentify an image to be identified, ensuring accuracy in faceidentification.

In S23, figure information corresponding to a most similar target imagemost similar to the each face image may be determined as the initialfigure identification result for the each face image. According to howthe similarity is computed in the example, a most similar target imagemost similar to a face image may be a target image with a maximalsimilarity to the face image. That is, figure information correspondingto a target image with a minimal distance to the face image may bedetermined as the initial figure identification result for the faceimage, thereby implementing preliminary identification of the faceimage.

As an example, FIG. 3 is a diagram of distribution of face images inmultiple images to be identified. A circle may be used to represent aface image of an actual user A. A square may be used to represent a faceimage of an actual user B. FIG. 3 may represent initial figureidentification results for face images. A letter A or B in a white shape(circle or square) may be used to represent an initial figureidentification result for the face image. Black shapes may be used torepresent target images of the users A and B in the image identificationlibrary.

As another example, in a scene of application, a target image mostlylikely may be an old image of the user A. Therefore, an identificationresult may be wrong based on how identification is done as described. Asshown in FIG. 3, a dotted shape may show a face image with a wronginitial figure identification result. Therefore, to avoid the problemeffectively, herein, a face image in images to be identified may beidentified accurately through S14 and S15 as well as a feature relatingto the images to be identified.

In S14, the face images may be grouped as follows. A facial feature ofeach face image may be extracted, specifically in a mode as elaborated,which is not repeated here. Then, the multiple images to be identifiedmay be clustered based on the facial feature. Face images correspondingto facial features belonging to one cluster may be put into one group.

As an example, a Euclidean distance between two facial features may becomputed, thereby performing clustering by K-Means clustering orhierarchical clustering, which is not limited herein. With a solutionherein, face images may be clustered based on facial features thereof.Clusters of clustered face images are shown in FIG. 4. C1 and C2 maycorrespond to different clusters, respectively. In this way, with asolution herein, a face image in images to be identified is processed.Face images may be classified based on facial features in the images tobe identified. Face images corresponding to one figure may be put in onegroup, facilitating subsequent correction of an identification resultfor a face image in the images to be identified, providing datasupporting accuracy in face identification.

In S15, the target figure identification result for the each face imagein the each group may be determined according to the initial figureidentification result for the each face image in the each group asfollows. One or more figures corresponding to the each group and a countof each of the one or more figures may be determined according to theinitial figure identification result for the each face image in the eachgroup. A figure with a maximal count may be determined as a targetfigure. Information on the target figure may be determined as the targetfigure identification result for the each face image in the each group.

As shown in FIG. 3, a group C1 may correspond to a figure A and a figureB. A count of the figure A may be 7. A count of the figure B may be 4.Therefore, the figure A may be determined as the target figure. Thefigure A may be determined as the target figure identification resultfor each face image in the group C1. Similarly, it may be determinedthat a group C2 corresponds to a figure A and a figure B. A count of thefigure A may be 4. A count of the figure B may be 12. Therefore, thefigure B may be determined as the target figure. The figure B may bedetermined as the target figure identification result for the each faceimage in the group C2. The target figure identification result for faceimages in FIG. 3 determined as such is shown in FIG. 5. That is, atarget figure identification result for a face image in the group C1with an initial figure identification result of B may be corrected to beA. A target figure identification result for a face image in the groupC2 with an initial figure identification result of A may be corrected tobe B. Accordingly, face images corresponding to the figure A and thefigure B may be identified accurately.

In this way, with a solution herein, face images are grouped, therebyputting similar face images in one group. Then, a figure with a maximalcount in a group may be determined through a voting algorithm, ensuringaccuracy of a determined target figure. Furthermore, an identificationresult for a face image in the group is corrected via information on thetarget figure, effectively improving accuracy of an identificationresult for a face image in the group. In addition, by grouping, initialfigure identification results for face images may be corrected in batch,thereby improving efficiency in face identification, further improvinguser experience.

The method may further include an option as follows. An image to beidentified, to which the each face image in the each group belongs, anda target figure identification result corresponding to the each group,may be output, respectively. A target figure identification resultcorresponding to a group may be the target figure identification resultcorresponding to any face image in the group.

After a face image has been extracted from an image to be identified,the correspondence between the face image and the image to be identifiedmay be recorded. Therefore, after a target figure identification resultfor any face image in a group has been determined, an image to beidentified, to which a face image in the group belongs, and the targetfigure identification result corresponding to the group, may be output,respectively. According to the example, an image to be identified towhich a face image in the group C1 belongs and a target figureidentification result corresponding to the group C1, namely informationon the figure A, may be output. Exemplarily, an image to be identifiedmay be labeled with a face image indicated by the target figureidentification result. The group C2 may be output similarly, which isnot repeated here.

A selection instruction input by a user may be received. In response tothe selection instruction, an image in images to be identified thatcontains information on a figure indicated by the selection instructionmay be output. For example, the user may specify to output an image inimages to be identified that contains information on a figure A.Accordingly, after the target figure identification result for each faceimage has been determined, an image to be identified, to which a faceimage with the target figure identification result of the figure Abelongs, may be output.

An image to be identified, to which a face image in a group belongs, anda target figure identification result corresponding to the group, may beoutput in a preset order. For example, a user may organize a photo albumwhich may contain images of a user from various historical periods.Then, an age for a face image in the group may be computed based on acurrent age. Accordingly, images may be output in an order of ascendingages. What described is merely exemplary, and does not limit the subjectdisclosure.

In this way, with a solution herein, a face in an image to be identifiedis identified. Then, images to be identified may be output based onidentification results of the images to be identified according toclassification of target figure identification results, thereby allowinga user to distinguish an image corresponding to each target figure inthe images to be identified, and sort and output the images to beidentified, effectively reducing a sorting operation of the user,improving user experience.

The method may further include an option as follows. The target figureidentification result for the each face image in the multiple images tobe identified may be output, respectively. Each face image in the imagesto be identified may be labeled with the target figure identificationresult, i.e., information on the target figure. Exemplarily, thelabeling may be performed using a face frame. Information on the targetfigure may be the name or ID of the target figure. In this way, a usermay quickly determine each figure included in an image to be identifiedbased on results output, allowing the user to distinguish figures in theimage to be identified, while increasing diversity of results output bya face identification method herein, thereby widening a scope to whichthe method for identifying a face applies.

Embodiments herein further provide a device for identifying a face. Asshown in FIG. 6, the device 10 includes a receiving module 100, anextracting module 200, a first determining module 300, a grouping module400, and a second determining module 500. Of course, it should beunderstood that one or more of the modules described in thisspecification can be implemented by circuitry.

The receiving module 100 is configured for receiving multiple images tobe identified. Each of the multiple images includes a face image part.The extracting module 200 is configured for extracting each face imageof face images in the multiple images to be identified. The firstdetermining module 300 is configured for determining an initial figureidentification result of identifying a figure in the each face image bymatching a face in the each face image respectively to a face in atarget image in an image identification library.

The grouping module 400 is configured for grouping the face images. Thesecond determining module 500 is configured for determining a targetfigure identification result for each face image in each group accordingto the initial figure identification result for the each face image inthe each group. The grouping module may include a feature extractingsub-module and a clustering sub-module. The feature extractingsub-module may be configured for extracting a facial feature of eachface image.

The clustering sub-module may be configured for clustering the multipleimages to be identified based on the facial feature, and putting faceimages corresponding to facial features belonging to one cluster intoone group.

The first determining module may include a feature extractingsub-module, a first determining sub-module, and a second determiningsub-module. The feature extracting sub-module may be configured forextracting a facial feature of the each face image. The firstdetermining sub-module may be configured for determining a similaritybetween the each face image and the target image according to the facialfeature of the each face image and a facial feature of the target image.

The second determining sub-module may be configured for determiningfigure information corresponding to a most similar target image mostsimilar to the each face image as the initial figure identificationresult for the each face image. The feature extracting sub-module mayinclude a first extracting sub-module, a correcting sub-module, and asecond extracting sub-module. The first extracting sub-module may beconfigured for acquiring a key point corresponding to the each faceimage by performing key point extraction on the each face image. Thecorrecting sub-module may be configured for acquiring a target faceimage by correcting the each face image according to the key point.

The second extracting sub-module may be configured for acquiring thefacial feature corresponding to the each face image by performingfeature extraction according to the target face image. The seconddetermining module may include a third determining sub-module, a fourthdetermining sub-module, and a fifth determining sub-module. The thirddetermining sub-module may be configured for determining one or morefigures corresponding to the each group and a count of each of the oneor more figures according to the initial figure identification resultfor the each face image in the each group. The fourth determiningsub-module may be configured for determining a figure with a maximalcount as a target figure. The fifth determining sub-module may beconfigured for determining information on the target figure as thetarget figure identification result for the each face image in the eachgroup.

The device may further include a first output module. The first outputmodule may be configured for respectively outputting an image to beidentified to which the each face image in the each group belongs and atarget figure identification result corresponding to the each group.

The device may further include a second output module. The second outputmodule may be configured for respectively outputting the target figureidentification result for the each face image in the multiple images tobe identified.

A module of the device according to at least one embodiment herein mayperform an operation in a mode elaborated in at least one embodiment ofthe method herein, which will not be repeated here.

According to an embodiment herein, a non-transitory computer-readablestorage medium has stored thereon computer program instructions which,when executed by a processor, implement an option in a method foridentifying a face herein.

FIG. 7 is a block diagram of a device 800 for identifying a faceaccording to an exemplary embodiment. For example, the device 800 may bea mobile phone, a computer, digital broadcasting UE, a messagetransceiver, a game console, tablet equipment, medical equipment,fitness equipment, a personal digital assistant, etc.

Referring to FIG. 7, the device 800 may include at least one of aprocessing component 802, memory 804, a power supply component 806, amultimedia component 808, an audio component 810, an Input/Output (I/O)interface 812, a sensor component 814, or a communication component 816.

The processing component 802 may generally control an overall operationof the device 800, such as operations associated with display, atelephone call, data communication, a camera operation, a recordingoperation, and the like. The processing component 802 may include one ormore processors 820 to execute instructions so as to complete all orpart of the options of an aforementioned method. In addition, theprocessing component 802 may include one or more modules to facilitateinteraction between the processing component 802 and other components.For example, the processing component 802 may include a multimediamodule to facilitate interaction between the multimedia component 808and the processing component 802.

The memory 804 may be configured for storing various types of data tosupport the operation at the device 800. Examples of such data mayinclude instructions of any application or method configured foroperating on the device 800, contact data, phonebook data, messages,pictures, videos, and the like. The memory 804 may be actualized by anytype of transitory or non-transitory storage equipment or a combinationthereof, such as Static Random Access Memory (SRAM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), Erasable ProgrammableRead-Only Memory (EPROM), Programmable Read-Only Memory (PROM),Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk,a compact disk, and the like.

The power supply component 806 may supply electric power to variouscomponents of the device 800. The power supply component 806 may includea power management system, one or more power sources, and othercomponents related to generating, managing, and distributing electricityfor the device 800.

The multimedia component 808 may include a screen that provides anoutput interface between the device 800 and a user. The screen mayinclude a Liquid Crystal Display (LCD) and a Touch Panel (TP). If thescreen may include a TP, the screen may be actualized as a touch screento receive a signal input by a user. The TP may include one or moretouch sensors for sensing touch, slide, and gestures on the TP. The oneor more touch sensors not only may sense the boundary of a touch orslide move, but also detect the duration and pressure related to thetouch or slide move. The multimedia component 808 may include at leastone of a front camera or a rear camera. When the device 800 is in anoperation mode such as a photographing mode or a video mode, at leastone of the front camera or the rear camera may receive externalmultimedia data. Each of the front camera or the rear camera may be afixed optical lens system or may have a focal length and be capable ofoptical zooming.

The audio component 810 may be configured for outputting and/orinputting an audio signal. For example, the audio component 810 mayinclude a microphone (MIC). When the device 800 is in an operation modesuch as a call mode, a recording mode, a voice identification mode, andthe like, the MIC may be configured for receiving an external audiosignal. The received audio signal may be further stored in the memory804 or may be sent via the communication component 816. The audiocomponent 810 may further include a loudspeaker configured foroutputting the audio signal.

The I/O interface 812 may provide an interface between the processingcomponent 802 and a peripheral interface module. Such a peripheralinterface module may be a keypad, a click wheel, a button, and the like.Such a button may include but is not limited to at least one of ahomepage button, a volume button, a start button, or a lock button.

The sensor component 814 may include one or more sensors for assessingvarious states of the device 800. For example, the sensor component 814may detect an on/off state of the device 800 and relative location ofcomponents such as the display and the keypad of the device 800. Thesensor component 814 may further detect a change in the location of thedevice 800 or of a component of the device 800, whether there is contactbetween the device 800 and a user, the orientation oracceleration/deceleration of the device 800, a change in the temperatureof the device 800, and the like. The sensor component 814 may include aproximity sensor configured for detecting existence of a nearby objectwithout physical contact. The sensor component 814 may further includean optical sensor such as a Complementary Metal-Oxide-Semiconductor(CMOS) or a Charge-Coupled-Device (CCD) image sensor used in an imagingapplication. The sensor component 814 may further include anacceleration sensor, a gyroscope sensor, a magnetic sensor, a pressuresensor, a temperature sensor, and the like.

The communication component 816 may be configured for facilitating wiredor wireless communication between the device 800 and other equipment.The device 800 may access a wireless network based on any communicationstandard such as Wi-Fi, 2G, 3G, . . . , or a combination thereof. Thecommunication component 816 may broadcast related information or receivea broadcast signal from an external broadcast management system via abroadcast channel. The communication component 816 may include a NearField Communication (NFC) module for short-range communication. Forexample, the NFC module may be based on technology such as RadioFrequency Identification (RFID), Infrared Data Association (IrDA),Ultra-Wideband (UWB) technology, Bluetooth (BT), and the like.

The device 800 may be actualized by one or more electronic componentssuch as an Application Specific Integrated Circuit (ASIC), a DigitalSignal Processor (DSP), a Digital Signal Processing Device (DSPD), aProgrammable Logic Device (PLD), a Field Programmable Gate Array (FPGA),a controller, a microcontroller, a microprocessor, and the like, toimplement an aforementioned method.

A transitory or non-transitory computer-readable storage mediumincluding instructions, such as memory 804 including instructions, maybe provided. The instructions may be executed by the processor 820 ofthe device 800 to implement an aforementioned method. For example, thecomputer-readable storage medium may be Read-Only Memory (ROM), RandomAccess Memory (RAM), Compact Disc Read-Only Memory (CD-ROM), a magnetictape, a floppy disk, optical data storage equipment, and the like.

In another exemplary embodiment, a computer program product may includea computer program that can be executed by a device capable ofprogramming. The computer program may have a code part which, whenexecuted by the programming device, implements a method for identifyinga face herein.

FIG. 8 is a block diagram of a device 1900 for identifying a faceaccording to an exemplary embodiment. For example, the device 1900 maybe provided as a server. Referring to FIG. 8, the device 1900 mayinclude a processing component 1922. The processing component mayinclude one or more processors. The device may include a memory resourcerepresented by memory 1932. The memory resource may be configured forstoring an instruction executable by the processing component 1922, suchas an APP. The APP stored in the memory 1932 may include one or moremodules. Each of the one or more modules may correspond to a group ofinstructions. In addition, the processing component 1922 may beconfigured for executing instructions to perform the method herein.

The device 1900 may further include a power supply component 1926. Thepower supply component may be configured for managing power of thedevice 1900. The device may further include a wired or wireless networkinterface 1950 configured for connecting the device 1900 to a network.The device may further include an Input/Output (I/O) interface 1958. Thedevice 1900 may operate based on an operating system stored in thememory 1932, such as a Windows Server™, a Mac OS X™, a Unix™, a Linux™,a FreeBSD™, and the like.

Further note that herein by “multiple”, it may mean two or more. Otherquantifiers may have similar meanings. A term “and/or” may describe anassociation between associated objects, indicating three possiblerelationships. For example, by A and/or B, it may mean that there may bethree cases, namely, existence of but A, existence of both A and B, orexistence of but B. A slash mark “I” may generally denote an “or”relationship between two associated objects that come respectivelybefore and after the slash mark. Singulars “a/an”, “said” and “the” areintended to include the plural form, unless expressly illustratedotherwise by context.

Further note that although in drawings herein operations are describedin a specific or der, it should not be construed as that the operationshave to be performed in the specific or der or sequence, or that anyoperation shown has to be performed in or der to acquire an expectedresult. Under a specific circumstance, multitask and parallel processingmay be advantageous.

Other implementations of the subject disclosure will be apparent to afigure having ordinary skill in the art that has considered thespecification and or practiced the subject disclosure. The subjectdisclosure is intended to cover any variation, use, or adaptation of thesubject disclosure following the general principles of the subjectdisclosure and including such departures from the subject disclosure ascome within common knowledge or customary practice in the art. Thespecification and the embodiments are intended to be exemplary only,with a true scope and spirit of the subject disclosure being indicatedby the appended claims.

Note that the subject disclosure is not limited to the exactconstruction that has been described above and illustrated in theaccompanying drawings, and that various modifications and changes can bemade to the subject disclosure without departing from the scope of thesubject disclosure. It is intended that the scope of the subjectdisclosure is limited only by the appended claims.

What is claimed is:
 1. A method for identifying a face, comprising:receiving multiple images to be identified, each of the multiple imagesincluding a face image part; extracting each face image of face imagesin the multiple images to be identified; determining an initial figureidentification result of identifying a figure in the each face image bymatching a face in the each face image respectively to a face in atarget image in an image identification library; grouping the faceimages; and determining a target figure identification result for eachface image in each group based on the initial figure identificationresult for the each face image in the each group.
 2. The method of claim1, wherein grouping the face images further comprises: extracting afacial feature of each face image; clustering the multiple images to beidentified based on the facial feature; and putting face imagescorresponding to facial features belonging to one cluster into onegroup.
 3. The method of claim 1, wherein determining the initial figureidentification result of identifying the figure in the each face imageby matching the face in the each face image respectively to the face inthe target image in the image identification library further comprises:extracting a facial feature of the each face image; determining asimilarity between the each face image and the target image based on thefacial feature of the each face image and a facial feature of the targetimage; and determining figure information corresponding to a mostsimilar target image that is most similar to the each face image as theinitial figure identification result for the each face image.
 4. Themethod of claim 2, wherein extracting the facial feature of the eachface image further comprises: acquiring a key point corresponding to theeach face image by performing key point extraction on the each faceimage; acquiring a target face image by correcting the each face imagebased on the key point; and acquiring the facial feature correspondingto the each face image by performing feature extraction based on thetarget face image.
 5. The method of claim 1, wherein determining thetarget figure identification result for the each face image in the eachgroup according to the initial figure identification result for the eachface image in the each group comprises: determining one or more figurescorresponding to the each group and a count of each of the one or morefigures according to the initial figure identification result for theeach face image in the each group; determining a figure with a maximalcount as a target figure; and determining information on the targetfigure as the target figure identification result for the each faceimage in the each group.
 6. The method of claim 1, further comprising:respectively outputting an image to be identified to which the each faceimage in the each group belongs and a target figure identificationresult corresponding to the each group.
 7. The method of claim 1,further comprising: respectively outputting the target figureidentification result for the each face image in the multiple images tobe identified.
 8. The method of claim 3, wherein extracting the facialfeature of the each face image further comprises: acquiring a key pointcorresponding to the each face image by performing key point extractionon the each face image; acquiring a target face image by correcting theeach face image based on the key point; and acquiring the facial featurecorresponding to the each face image by performing feature extractionaccording to the target face image.
 9. A device for identifying a face,comprising a processor and memory that is configured to storeinstructions executable by the processor, wherein the processor isconfigured to perform operations comprising: receiving multiple imagesto be identified, each of the multiple images including a face imagepart; extracting each face image of face images in the multiple imagesto be identified; determining an initial figure identification result ofidentifying a figure in the each face image by matching a face in theeach face image respectively to a face in a target image in an imageidentification library; grouping the face images; and determining atarget figure identification result for each face image in each groupbased on the initial figure identification result for the each faceimage in the each group.
 10. The device of claim 9, wherein theprocessor is configured to perform operations further comprising:extracting a facial feature of each face image; clustering the multipleimages to be identified based on the facial feature; and putting faceimages corresponding to facial features belonging to one cluster intoone group.
 11. The device of claim 9, wherein the processor isconfigured to perform operations further comprising: extracting a facialfeature of the each face image; determining a similarity between theeach face image and the target image based on the facial feature of theeach face image and a facial feature of the target image; anddetermining figure information corresponding to a most similar targetimage that is most similar to the each face image as the initial figureidentification result for the each face image.
 12. The device of claim10, wherein the processor is configured to perform operations furthercomprising: acquiring a key point corresponding to the each face imageby performing key point extraction on the each face image; acquiring atarget face image by correcting the each face image according to the keypoint; and acquiring the facial feature corresponding to the each faceimage by performing feature extraction based on the target face image.13. The device of claim 9, wherein the processor is configured toperform operations further comprising: determining one or more figurescorresponding to the each group and a count of each of the one or morefigures according to the initial figure identification result for theeach face image in the each group; determining a figure with a maximalcount as a target figure; and determining information on the targetfigure as the target figure identification result for the each faceimage in the each group.
 14. The device of claim 9, wherein theprocessor is configured to perform operations further comprising:respectively outputting an image to be identified to which the each faceimage in the each group belongs and a target figure identificationresult corresponding to the each group.
 15. The device of claim 9,wherein the processor is configured to perform operations furthercomprising: respectively outputting the target figure identificationresult for the each face image in the multiple images to be identified.16. The device of claim 11, wherein the processor is configured toperform operations further comprising: acquiring a key pointcorresponding to the each face image by performing key point extractionon the each face image; acquiring a target face image by correcting theeach face image according to the key point; and acquiring the facialfeature corresponding to the each face image by performing featureextraction based on the target face image.
 17. A non-transitorycomputer-readable storage medium, having stored therein computer programinstructions which, when executed by a processor, cause the processor toimplement operations comprising: receiving multiple images to beidentified, each of the multiple images including a face image part;extracting each face image of face images in the multiple images to beidentified; determining an initial figure identification result ofidentifying a figure in the each face image by matching a face in theeach face image respectively to a face in a target image in an imageidentification library; grouping the face images; and determining atarget figure identification result for each face image in each groupbased on the initial figure identification result for the each faceimage in the each group.
 18. The storage medium of claim 17, whereingrouping the face images further comprises: extracting a facial featureof each face image; clustering the multiple images to be identifiedbased on the facial feature; and putting face images corresponding tofacial features belonging to one cluster into one group.
 19. The storagemedium of claim 17, wherein determining the initial figureidentification result of identifying the figure in the each face imageby matching the face in the each face image respectively to the face inthe target image in the image identification library further comprises:extracting a facial feature of the each face image; determining asimilarity between the each face image and the target image base on thefacial feature of the each face image and a facial feature of the targetimage; and determining figure information corresponding to a mostsimilar target image most similar to the each face image as the initialfigure identification result for the each face image.
 20. The storagemedium of claim 18, wherein extracting the facial feature of the eachface image comprises: acquiring a key point corresponding to the eachface image by performing key point extraction on the each face image;acquiring a target face image by correcting the each face imageaccording to the key point; and acquiring the facial featurecorresponding to the each face image by performing feature extractionbased on the target face image.